Brief communication: Body mass index,body adiposity index,and percent body fat in asians

Human obesity is a growing epidemic throughout the world. Body mass index (BMI) is commonly used as a good indicator of obesity. Body adiposity index (BAI = hip circumference (cm)/stature (m)^1.5 ? 18), as a new surrogate measure, has been proposed recently as an alternative to BMI. This study, for the first time, compares BMI and BAI for predicting percent body fat (PBF; estimated from skinfolds) in a sample of 302 Buryat adults (148 men and 154 women) living in China. The BMI and BAI were strongly correlated with PBF in both men and women. The correlation coefficient between BMI and PBF was higher than that between BAI and PBF for both sexes. For the linear regression analysis, BMI better predicted PBF in both men and women; the variation around the regression lines for each sex was greater for BAI comparisons. For the receiver operating characteristic (ROC) analysis, the area under the ROC curve for BMI was higher than that for BAI for each sex, which suggests that the discriminatory capacity of the BMI is higher than the one of BAI. Taken together, we conclude that BMI is a more reliable indicator of PBF derived from skinfold thickness in adult Buryats. Am J Phys Anthropol 152:294–299, 2013. © 2013 Wiley Periodicals, Inc.     

Concordance of the recently published body adiposity index with measured body fat percent in European-American adults

The body adiposity index (BAI; hip circumference (cm)/height (m)(1.5) - 18) has recently been shown to demonstrate a stronger correlation with percentage body fat (%fat) than that between the BMI and %fat in Mexican-American adults. Here, we compare the concordance between %fat from dual-energy X-ray absorptiometry (DXA) and BAI, and between %fat and BMI, in European-American adults (n = 623). Agreement between BAI, BMI, and %fat was assessed using Lin's concordance coefficients (ρ(c)), where values <0.90 are considered poor. In the sample as a whole, the agreement between BAI and %fat (ρ(c) = 0.752) was far better than that between BMI and %fat (ρ(c) = 0.445) but was nonetheless relatively poor. There were large mean differences in %fat between the BAI and DXA %fat, particularly at lower levels of adiposity (<20%), and further the BAI overestimated %fat in males and underestimated %fat in females. Optimizing the BAI formula for our sample only marginally improved performance. Results of the present study show that BAI provides a better indicator of adiposity in European-American adults than does BMI, but does not provide valid estimates of %fat, particularly at lower levels of body fatness. Further research is warranted to investigate the predictive ability of BAI for various health outcomes.     

A new method for body fat evaluation, body adiposity index, is useful in women with familial partial lipodystrophy

BMI is a widely used method to evaluate adiposity. However, it has several limitations, particularly an inability to differentiate lean from fat mass. A new method, body adiposity index (BAI), has been recently proposed as a new measurement capable to determine fat excess better than BMI. The aim of this study was to investigate BAI as a mean to evaluate adiposity in a group of women with familial partial lipodystrophy (FPLD) and compare it with BMI. Thirteen women with FLPD Dunnigan type (FPLD2) and 13 healthy volunteers matched by age and BMI were studied. Body fat content and distribution were analyzed by dual X-ray absorptiometry (DXA). Plasma leptin was also measured. BAI was significantly lower in FPLD2 in comparison to control group (24.6 ± 1.5 vs. 30.4 ± 4.3; P < 0.001) and presented a more significant correlation with total fat (%) (r = 0.71; P < 0.001) and fat Mass (g) (r = 0.80; P < 0.001) than BMI (r = 0.27; P = 0.17 for total fat and r = 0.52; P = 0.006 for fat mass). There was a correlation between leptin and BAI (r = 0.57; P = 0.01), [corrected] but not between leptin and BMI. In conclusion, BAI was able to catch differences in adiposity in a sample of FPLD2 patients. It also correlated better with leptin levels than BMI. Therefore, we provide further evidence that BAI may become a more reliable indicator of fat mass content than the currently available measurements.     

For better or worse: Relationship status and body mass index

Recent increases in the incidence of obesity and declines in marriage have prompted policymakers to implement policies to mitigate these trends. This paper examines the link between these two outcomes. There are four hypotheses (selection, protection, social obligation and marriage market) that might explain the relationship between marital status transitions and changes in Body Mass Index (BMI). The selection hypothesis suggests that those with a lower BMI are more likely to be selected into marriage. The protection hypothesis states that married adults will have better physical health as a result of the increased social support and reduced incidence of risky behavior among married individuals. The social obligation hypothesis states that those in relationships may eat more regular meals and/or richer and denser foods due to social obligations which may arise because of marriage. Finally, the marriage market hypothesis indicates that when adults are no longer in the marriage market they may not maintain a healthy BMI because doing so is costly and they are in a stable union—or on the other hand, adults may enhance their prospects in the marriage market by losing weight. Taking advantage of longitudinal data and complete marriage histories in the National Longitudinal Survey of Youth 1979, we estimate individual fixed effects models to examine associations between the change in log BMI and the incidence of overweight and obesity, and changes in relationship status controlling for the effects of aging and other respondent characteristics. We find no support for the marriage protection hypothesis. Rather we find evidence supporting the social obligation and marriage market hypotheses—BMI increases for both men and women during marriage and in the course of a cohabiting relationship. Separate analyses by race and ethnicity reveal substantial differences in the response of BMI to relationship status across these groups.     

Body adiposity index assess body fat with high accuracy in nondialyzed chronic kidney disease patients

Objective:

High body fat (BF) is an alarming condition that also affects nondialyzed chronic kidney disease (CKD) patients. Distinct methods are used to evaluate BF; however, in CKD population it remains unclear which one is more reliable showing high accuracy. Dual‐energy X‐ray absorptiometry (DXA), used as reference method to estimate adiposity, is expensive and time consuming to be applied in clinical settings. Recently, a new body adiposity index (BAI), that estimates BF from easily accessible measures, was validated in the general population. The aim of this study was to evaluate which simple and practical method, routinely used to estimate BF, shows the highest accuracy compared with DXA, in nondialyzed CKD patients.

Design and Methods:

In this cross‐sectional study BF was estimated by DXA, bioelectrical impedance analysis (BIA), anthropometry (ANTHRO), and BAI. Serum leptin levels were determined.

Results:

Studied patients (n = 134) were 55% males, 54% overweight/obese, and 64.9 ± 12.5 years old, with estimated glomerular filtration rate (eGFR) = 29.0 ± 12.7 ml/min. The correlation coefficient was higher between DXA vs. ANTHRO (r = 0.76) and BAI (r = 0.61) than with BIA (r = 0.57), after adjusting for gender, age, and eGFR (P < 0.0001). Therefore, the Lin's concordance correlation coefficient and Bland–Altman plots were performed to measure the accuracy (C_b) between DXA with both ANTHRO and BAI. A higher accuracy (C_b = 0.82) and lower mean difference (?3.4%) was observed for BAI than for ANTHRO (C_b = 0.61; ?8.4%). Leptin levels correlated (P < 0.0001) with DXA (r = 0.56) and BAI (r = 0.59).

Conclusions:

These findings suggest that BAI estimates BF with high accuracy in nondialyzed CKD patients and may be helpful in the treatment of this population with increased BF.

     

Measuring adiposity in patients: the utility of body mass index (BMI), percent body fat, and leptin

Background

Obesity is a serious disease that is associated with an increased risk of diabetes, hypertension, heart disease, stroke, and cancer, among other diseases. The United States Centers for Disease Control and Prevention (CDC) estimates a 20% obesity rate in the 50 states, with 12 states having rates of over 30%. Currently, the body mass index (BMI) is most commonly used to determine adiposity. However, BMI presents as an inaccurate obesity classification method that underestimates the epidemic and contributes to failed treatment. In this study, we examine the effectiveness of precise biomarkers and duel-energy x-ray absorptiometry (DXA) to help diagnose and treat obesity.

Methodology/Principal Findings

A cross-sectional study of adults with BMI, DXA, fasting leptin and insulin results were measured from 1998–2009. Of the participants, 63% were females, 37% were males, 75% white, with a mean age = 51.4 (SD = 14.2). Mean BMI was 27.3 (SD = 5.9) and mean percent body fat was 31.3% (SD = 9.3). BMI characterized 26% of the subjects as obese, while DXA indicated that 64% of them were obese. 39% of the subjects were classified as non-obese by BMI, but were found to be obese by DXA. BMI misclassified 25% men and 48% women. Meanwhile, a strong relationship was demonstrated between increased leptin and increased body fat.

Conclusions/Significance

Our results demonstrate the prevalence of false-negative BMIs, increased misclassifications in women of advancing age, and the reliability of gender-specific revised BMI cutoffs. BMI underestimates obesity prevalence, especially in women with high leptin levels (>30 ng/mL). Clinicians can use leptin-revised levels to enhance the accuracy of BMI estimates of percentage body fat when DXA is unavailable.     

25-Hydroxyvitamin D levels of children are inversely related to adiposity assessed by body mass index

Vitamin D deficiency is associated with wide range of pathologies. Some evidences have shown that low vitamin D circulating levels in children and adolescent are related to fat mass and obesity. The objectives of the present study were to characterize vitamin D status in children and adolescents and to determine if serum 25-hydroxyvitamin D (25(OH)D) concentration is related to adiposity assessed by body mass index (BMI). Serum 25(OH)D levels were measured by LIAISON method in 471 children and adolescents (2 to 18 years age) and analyzed according to gender, pubertal period, age, and BMI. An overall prevalence of 25(OH)D insufficiency and deficiency was present in the 67.1%. Lower 25(OH)D levels were found in females (25.56 ± 14.03 vs 29.71 ± 17.10 ng ml^?1; P = 0.004) and pubertal children (25.52 ± 13.97 vs 29.21 ± 16.83 ng ml^?1; P = 0.011). In addition, an inverse relation of BMI and age on 25(OH)D concentrations was observed in children. In conclusion, low vitamin D status was highly prevalent among children and adolescents. Of note, a non-lineal regression model showed that 39.6% of vitamin D levels variability was explained by BMI. These results indicate that adiposity assessed by BMI impacts vitamin D status.     

Comparison of body adiposity index (BAI) and bmi with estimations of % body fat in clinically severe obese women

Objective:

Body adiposity index (BAI), a new surrogate measure of body fat (hip circumference/(height^1.5 – 18)), has been proposed as an alternative to body mass index (BMI). We compared BAI with BMI, and each of them with laboratory measures of body fat‐derived from bioimpedance analysis (BIA), air displacement plethysmography (ADP), and dual‐energy X‐ray absorptiometry (DXA) in clinically severe obese (CSO) participants.

Design and Methods:

Nineteen prebariatric surgery CSO, nondiabetic women were recruited (age = 32.6 ± 7.7 SD; BMI = 46.5 ± 9.0 kg/m²). Anthropometrics and body fat percentage (% fat) were determined from BIA, ADP, and DXA. Scatter plots with lines of equality and Bland–Altman plots were used to compare BAI and BMI with % fat derived from BIA, ADP, and DXA. BAI and BMI correlated highly with each other (r = 0.90, P < 0.001).

Results:

Both BAI and BMI correlated significantly with % fat from BIA and ADP. BAI, however, did not correlate significantly with % fat from DXA (r = 0.42, P = 0.08) whereas BMI did (r = 0.65, P = 0.003). BMI was also the single best predictor of % fat from both BIA (r² = 0.80, P < 0.001) and ADP (r² = 0.65, P < 0.001). The regression analysis showed that the standard error of the estimate (SEE), or residual error around the regression lines, was greater for BAI comparisons than for BMI comparisons with BIA, ADP, and DXA. Consistent with this, the Bland and Altman plots indicated wider 95% confidence intervals for BAI difference comparisons than for BMI difference comparisons for their respective means for BIA, ADP, and DXA.

Conclusions:

Thus, BAI does not appear to be an appropriate proxy for BMI in CSO women.     

A new body shape index predicts mortality hazard independently of body mass index

Background

Obesity, typically quantified in terms of Body Mass Index (BMI) exceeding threshold values, is considered a leading cause of premature death worldwide. For given body size (BMI), it is recognized that risk is also affected by body shape, particularly as a marker of abdominal fat deposits. Waist circumference (WC) is used as a risk indicator supplementary to BMI, but the high correlation of WC with BMI makes it hard to isolate the added value of WC.

Methods and Findings

We considered a USA population sample of 14,105 non-pregnant adults () from the National Health and Nutrition Examination Survey (NHANES) 1999–2004 with follow-up for mortality averaging 5 yr (828 deaths). We developed A Body Shape Index (ABSI) based on WC adjusted for height and weight: ABSI had little correlation with height, weight, or BMI. Death rates increased approximately exponentially with above average baseline ABSI (overall regression coefficient of per standard deviation of ABSI [95% confidence interval: –]), whereas elevated death rates were found for both high and low values of BMI and WC. (–) of the population mortality hazard was attributable to high ABSI, compared to (–) for BMI and (–) for WC. The association of death rate with ABSI held even when adjusted for other known risk factors including smoking, diabetes, blood pressure, and serum cholesterol. ABSI correlation with mortality hazard held across the range of age, sex, and BMI, and for both white and black ethnicities (but not for Mexican ethnicity), and was not weakened by excluding deaths from the first 3 yr of follow-up.

Conclusions

Body shape, as measured by ABSI, appears to be a substantial risk factor for premature mortality in the general population derivable from basic clinical measurements. ABSI expresses the excess risk from high WC in a convenient form that is complementary to BMI and to other known risk factors.     

Physical exercise, body mass index, subcutaneous adiposity and body composition among Bengalee boys aged 10-17 years of Kolkata, India

A comparative study of 215 sedentary (no regular physical exercise undertaken) and 313 physically active (regular physical exercise undertaken) Bengalee boys aged 10-17 years was undertaken to investigate the differences in overall adiposity (body mass index), subcutaneous adiposity (skinfolds) and body composition (percent body fat, fat mass and fat mass index). Both groups had a similar age. The results revealed that boys who did not undertake regular physical exercise (NPE) had a significantly greater mean body mass index (BMI) compared with those who undertook regular physical exercise (PE); p < 0.001. The means for all the skinfolds as well as percent body fat (PBF), fat mass (FM) and fat mass index (FMI) were significantly higher among the NPE group. The percentile distributions of all these variables and indices were consistently higher among the NPE group. The results of ANOVA of physical exercise (PE = yes, NPE = no) and PBF, FM and FMI, with age as covariate, revealed that PE had a significant negative effect on all these measures of body composition even after controlling for the impact of age. The means in each case were greater among the NPE group. In conclusion, this study provided evidence that Bengalee boys, who undertook regular physical exercise, had significantly less adiposity compared with those who did not undertake regular physical exercise.     

Body adiposity index and other indexes of body composition in the SAPHIR study: Association with cardiovascular risk factors

Objective:

The accuracy of anthropometric surrogate markers such as the body adiposity index (BAI) and other common indexes like the body mass index (BMI), waist‐to‐hip ratio (WHR) and waist‐to‐height ratio (WHtR) to predict metabolic sequelae is essential for its use in clinical practice.

Design and Methods:

Thus, we evaluated the strength of BAI and other indexes to relate with anthropometric parameters, adipocytokines, blood lipids, parameters of glucose‐homeostasis and blood pressure in 1,770 patients from the Salzburg Atherosclerosis Prevention Program in Subjects at High Individual Risk (SAPHIR) study in a crosssectional design. Measurements were BAI, BMI, WHR, WHtR, abdominal subcutaneous and visceral adipose tissue (aSAT and VAT), total body adipose tissue mass, body weight, waist‐ and hip circumference (WC and HC), leptin, adiponectin, high‐density lipoprotein‐cholesterol (HDL‐C), low‐density lipoprotein‐cholesterol (LDL‐C), triglycerides (TG), fasting plasma glucose, fasting plasma insulin, the homeostasis model assessment of insulin resistance (HOMAIR), systolic and diastolic blood pressure.

Results and Conclusions:

BAI was significantly associated with leptin and HC. We conclude that BAI was the best calculator for leptin. BAI was inferior to BMI to predict anthropometric parameters other than HC, adiponectin, blood lipids, parameters of glucose homeostasis, and blood pressure in this cross‐sectional study.